{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "1. test data that fit the train data well.\n",
    "2. train data that fit the test data well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 145 / 500 correct => accuracy: 0.290000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "5\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "For knn,all pixel are euqal not matter what kind of rotating they are all vector for knn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 43.072154 seconds\n",
      "One loop version took 88.887235 seconds\n",
      "No loop version took 0.921469 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.257000\n",
      "k = 3, accuracy = 0.263000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.282000\n",
      "k = 3, accuracy = 0.270000\n",
      "k = 5, accuracy = 0.265000\n",
      "k = 5, accuracy = 0.275000\n",
      "k = 5, accuracy = 0.295000\n",
      "k = 5, accuracy = 0.298000\n",
      "k = 5, accuracy = 0.284000\n",
      "k = 8, accuracy = 0.272000\n",
      "k = 8, accuracy = 0.295000\n",
      "k = 8, accuracy = 0.284000\n",
      "k = 8, accuracy = 0.298000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 10, accuracy = 0.272000\n",
      "k = 10, accuracy = 0.303000\n",
      "k = 10, accuracy = 0.289000\n",
      "k = 10, accuracy = 0.292000\n",
      "k = 10, accuracy = 0.285000\n",
      "k = 12, accuracy = 0.271000\n",
      "k = 12, accuracy = 0.305000\n",
      "k = 12, accuracy = 0.285000\n",
      "k = 12, accuracy = 0.289000\n",
      "k = 12, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.260000\n",
      "k = 15, accuracy = 0.302000\n",
      "k = 15, accuracy = 0.292000\n",
      "k = 15, accuracy = 0.292000\n",
      "k = 15, accuracy = 0.285000\n",
      "k = 20, accuracy = 0.268000\n",
      "k = 20, accuracy = 0.293000\n",
      "k = 20, accuracy = 0.291000\n",
      "k = 20, accuracy = 0.287000\n",
      "k = 20, accuracy = 0.286000\n",
      "k = 50, accuracy = 0.273000\n",
      "k = 50, accuracy = 0.291000\n",
      "k = 50, accuracy = 0.274000\n",
      "k = 50, accuracy = 0.267000\n",
      "k = 50, accuracy = 0.273000\n",
      "k = 100, accuracy = 0.261000\n",
      "k = 100, accuracy = 0.272000\n",
      "k = 100, accuracy = 0.267000\n",
      "k = 100, accuracy = 0.260000\n",
      "k = 100, accuracy = 0.267000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "X_train_folds.extend(np.array_split(X_train,num_folds))\n",
    "y_train_folds.extend(np.array_split(y_train,num_folds))\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k]=[]\n",
    "    for i in range(num_folds):\n",
    "        classifier.train(np.concatenate([X_train_folds[j] for j in range(num_folds) if j!=i]),np.concatenate([y_train_folds[j] for j in range(num_folds) if j!=i]))\n",
    "        k_to_accuracies[k].append(1.0*np.sum(classifier.predict(X_train_folds[i],k=k)==y_train_folds[i])/len(y_train_folds[i]))\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 144 / 500 correct => accuracy: 0.288000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "4\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "Because knn just simply remember the training data,and need to compare test data to each one of training data,if training data is too large,\n",
    "the performance will be bad soon.\n"
   ]
  }
 ],
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